GEA_Partial_Recombination_Analysis
16/06/2024
1 GEA Partial Recombination Analysis
2 Objective
Take a look at the GEA line that was Partially recombined and check if it was successful for a following Nanopore sequencing
3 Analysis
The dataset conditions is as follows
## Mouse Recomb Tissue Injection Substance Processing
## 1 GEA107 FlpOERT2xEMT Bone Marrow P5 14/12/23 4OHT Std 22Jan23
## 2 GEA107,110,111 FlpOERT2xEMT Liver, Lung, Heart P5 14/12/23 4OHT Std 22Jan23
## 3 GEA99 VEQxiHadSureCre Liver, Lung 26-27/01/24 Tamox Std 02Feb243.1 Module Scores of the iFlpscLineage cassette
First We will look at the Barcodes grouped to see how the expression rates change
## [1] "GEA107_FlpOERT2xEMT" "GEA99_VEQxAJD" "GEA107_110_111_FlpOERT2xEMT_Neg"
Violin Plots show that Array3 has the highest rate of expression
## TableGrob (5 x 5) "guide-box": 2 grobs
## z cells name grob
## 1 1 (3-3,3-3) guides gtable[layout]
## 2 0 (2-4,2-4) legend.box.background zeroGrob[NULL]
We can also look at the % of cells expressing iFlpscLineage in the dataset
3.2 Looking at Expression rates of iFlpscLinage per cell
Now in terms of % of counts in the dataset
The expression rate is much more modest in the Adult mice than in Embryos. FlpOERT2xEMT show similar expression rates as VEQxAJD even though injections were performed a week before and Tamoxifen instead of 4OHT
We can take a look at rates of expression among arrays
Let’s check the number and percentage of cells expressing more than one Array in the dataset
Now the difference of levels of expression among arrays is much more than with the embryos. Array 3 is much more expressed than the others, then Array 2 and Array1
3.3 Looking at the Distribution of FRT-BCs throughout the populations
3.3.1 Looking at FRT-BC Mean Counts per cell
3.3.2 Looking at FRT-BC Counts
And Now instead of Mean counts per cell lets look at the total counts in the dataset
Seeing that most reads fall in the A-0 BC indicates mostly unrecombined BCs, but specially on A3 you can see counts of BCs that can indicate Partial recombination. A1 Array seems to not have undergone much recombination. FlpOERT2xEMT show much higher recombination rates than VEQxAJD
3.4 Doing a ratio analysis between Fwd and Inverted BC segment
The ratio analysis between Inv/Fwd BC segments indicate a good level of Iverted BCs present in the dataset
3.5 Looking at the Distribution of other transgenes in the populations
3.5.1 Looking at Transgene Counts
And Now instead of Mean counts per cell lets look at the total counts in the dataset
3.6 Looking at the other Transgenes present in the dataset
4 Conclusion
Here we look at Partial recombination using FlpOERT2xEMT and VEQxiHadSureCre strategies. The FlpOERT2xEMT was induced twice with Std Dose Tamoxifen one week before processing. VEQxiHadSureCre were induced at P5 with a Std 4OHT Dose and Processed a month and a week later. Overall, detection and recombination is higher in Array3, then 2 then 1, no matter the group. Detection of BCs is higher in FlpOERT2xEMT than in VEQxiSureHadCre and the same with the recombination rate.
Until we do not do Nanopore sequencing we will not be certain of the extent of the Partial Recombination. While in VEQxiSureHadCre it does not look very promising, FlpOERT2xEMT could have a good level of Partially recombined BCs.
NOTE: The group GEA107_110_111_FlpOERT2xEMT_Neg was produced with the Negative Hashtag group. The CMO used for identifying this group failed (CMO311), so mosto (but not all) negative cells belong to this group.
5 R session
## R version 4.3.3 (2024-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 24.04 LTS
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
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## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Madrid
## tzcode source: system (glibc)
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] cowplot_1.1.3 RColorBrewer_1.1-3 tidyr_1.3.1 patchwork_1.2.0 yaml_2.3.10 rmarkdown_2.27 dplyr_1.1.4 ggplot2_3.5.1 Seurat_5.1.0 SeuratObject_5.0.2 sp_2.1-4 knitr_1.48 BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] deldir_2.0-4 pbapply_1.7-2 gridExtra_2.3 rlang_1.1.4 magrittr_2.0.3 RcppAnnoy_0.0.22 spatstat.geom_3.3-2 matrixStats_1.3.0 ggridges_0.5.6 compiler_4.3.3 png_0.1-8 vctrs_0.6.5 reshape2_1.4.4 stringr_1.5.1 pkgconfig_2.0.3 fastmap_1.2.0 labeling_0.4.3 utf8_1.2.4 promises_1.3.0 tinytex_0.52 purrr_1.0.2 xfun_0.46 cachem_1.1.0 jsonlite_1.8.8 goftest_1.2-3 highr_0.11 later_1.3.2 spatstat.utils_3.0-5 irlba_2.3.5.1 parallel_4.3.3 cluster_2.1.6 R6_2.5.1 ica_1.0-3 spatstat.data_3.1-2 stringi_1.8.4 bslib_0.8.0 reticulate_1.38.0 spatstat.univar_3.0-0 parallelly_1.38.0 lmtest_0.9-40 jquerylib_0.1.4 scattermore_1.2 Rcpp_1.0.13 bookdown_0.40 tensor_1.5 future.apply_1.11.2 zoo_1.8-12 sctransform_0.4.1 httpuv_1.6.15 Matrix_1.6-5 splines_4.3.3 igraph_2.0.3 tidyselect_1.2.1 abind_1.4-5 rstudioapi_0.16.0 spatstat.random_3.3-1 spatstat.explore_3.3-1 codetools_0.2-19 miniUI_0.1.1.1 listenv_0.9.1 lattice_0.22-5 tibble_3.2.1 plyr_1.8.9 withr_3.0.1 shiny_1.9.1 ROCR_1.0-11 evaluate_0.24.0 Rtsne_0.17 future_1.34.0 fastDummies_1.7.3 survival_3.5-8 polyclip_1.10-7 fitdistrplus_1.2-1 pillar_1.9.0 BiocManager_1.30.23 KernSmooth_2.23-22 plotly_4.10.4 generics_0.1.3 RcppHNSW_0.6.0 munsell_0.5.1 scales_1.3.0 globals_0.16.3 xtable_1.8-4 glue_1.7.0 lazyeval_0.2.2 tools_4.3.3 data.table_1.15.4 RSpectra_0.16-2 RANN_2.6.1 leiden_0.4.3.1 dotCall64_1.1-1 grid_4.3.3 colorspace_2.1-1 nlme_3.1-164 cli_3.6.3 spatstat.sparse_3.1-0 spam_2.10-0 fansi_1.0.6 viridisLite_0.4.2 uwot_0.2.2 gtable_0.3.5 sass_0.4.9 digest_0.6.36 progressr_0.14.0 ggrepel_0.9.5 farver_2.1.2 htmlwidgets_1.6.4 htmltools_0.5.8.1 lifecycle_1.0.4 httr_1.4.7 mime_0.12 MASS_7.3-60.0.1